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Local Differential Privacy for Belief Functions
February 17, 2022 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Qiyu Li, Chunlai Zhou, Biao Qin, Zhiqiang Xu
arXiv ID
2202.08576
Category
cs.CR: Cryptography & Security
Citations
5
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
Abstract
In this paper, we propose two new definitions of local differential privacy for belief functions. One is based on Shafer's semantics of randomly coded messages and the other from the perspective of imprecise probabilities. We show that such basic properties as composition and post-processing also hold for our new definitions. Moreover, we provide a hypothesis testing framework for these definitions and study the effect of "don't know" in the trade-off between privacy and utility in discrete distribution estimation.
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